Computer
Asaad Raheem Kareem; Hasanen S. Abdullah
Abstract
The article provides an overview of two recent developments in technology: Business Intelligence (BI) and Deep Learning (DL). In order to support decision-making processes, BI entails gathering, integrating, and analyzing data from various sources, while DL uses artificial neural networks to learn and ...
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The article provides an overview of two recent developments in technology: Business Intelligence (BI) and Deep Learning (DL). In order to support decision-making processes, BI entails gathering, integrating, and analyzing data from various sources, while DL uses artificial neural networks to learn and generate predictions from complicated datasets. This paper introduces the concepts and principles and highlights recent developments and applications in different domains of research: education, organizations, stock market, forecasting, decision-making in real-time, and security. However, the fundamental problem with the business intelligence approach is that there is no learning involved. Other limitations and challenges include the capacity that affects the data analysis process, the variety of data in results, and the need for a complete presentation of results in the form of dashboards, scorecards, reports, and portals. The approach choice hinges on the problem's context and requirements and the nature and characteristics of the data. Although BI and DL are widespread, alternative methods may suit well too, such as machine learning, data mining, and statistical analysis. Justifying the selection based on precise needs and goals is crucial. Recurrent neural networks (RNN), convolutional neural networks (CNN), long short-term memory (LSTM), gated recurrent units (GRU), and Business intelligence tools are used in the research problem to address these limitations and explore the potential advantages and difficulties of integrating BI and DL to achieve an advantage in a given sector.
Computer
Mohammed Abdulwahid Jebur; Hasanen Abdullah
Abstract
The university courses timetabling problem (UCTP) is a big topic among academics and institutions since it occurs every academic year. In general, UCTP is the distribution of events across slots time for each room based on a list of restrictions provided in one semester, such as (hard constraint and ...
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The university courses timetabling problem (UCTP) is a big topic among academics and institutions since it occurs every academic year. In general, UCTP is the distribution of events across slots time for each room based on a list of restrictions provided in one semester, such as (hard constraint and soft constraint), with the objective of avoiding conflicts in such assignments. Hard constraints should never be breached when striving to satisfy as many soft constraints as possible. There are many different methods used in automating the problems of the university timetabling course in higher education institutions. This paper presents an improved algorithm for the Meerkat Clan to solve the UCTP. This is done by studying the behavior of the Meerkat Clan Algorithm and Specifying the points that are able to improve without changing the main behavior of the Meerkat Clan Algorithm. And by testing with four datasets of different sizes, good results were obtained by optimizing this algorithm.
